A vector quantization approach to speaker recognition
(1985)

CH2118-8/85/0000-0387 $1.00 © 1985 IEEE 387 ABSTRACT. In this study a vector quantIzation (VQ) codebook was system. In the other, Shore and Burton 112] used word-based VQ used as an efficient means of characterizing the short-time spectral codebooks and reported good performance in speaker-trained isolatedfeatures of a speaker. A set of such codebooks were then used to word recognition experiments. Here, instead of using word-based VQ recognize the identity of an unknown speaker from his/her unlabelled spoken utterances based on a minimum distance (distortion) codebooks to characterize the phonetic contents of isolated words, we propose to use speaker-based VQ codebooks to characterize the classification rule. A series of speaker recognition experiments was variability of short-time acoustic features of speakers. performed using a 100-talker (50 male and 50 female) telephone recording database consisting of isolated digit utterances. For ten random but different isolated digits, over 98% speaker identification H. Speaker-based VQ Codebook Approach to Speaker accuracy was achieved. The effects, on performance, of different Characterization and Recognition system parameters such as codebook sizes, the number of test digits, phonetic richness of the text, and difference in recording sessions Were also studied in detail. A set of short-time raw feature vectors of a speaker can be used directly to represent the essential acoustical, phonological or physiological characteristics of that speaker if the training set includes sufficient variations. However such a direct representation is not practical when the number of training vectors is large. The memory requirements for storage and computational complexity in the recognition phase eventually become prohibitively high. Therefore an Automatic speaker recognition has long been an interesting and challenging problem to speech researchers [1-101. The problem, efficient way of compressing the training data had to be found. In order to compress the original data to a small set of representative depending upon the nature of the final task, can be classified into two points, we used a VQ codebook with a small number of codebook different categories: speaker verification and speaker identification. In entries. a speaker verification task, the recognizer is asked to verify an identity claim made by an unknown speaker and a decision to reject or accept the identity claim is made. In a speaker identification task the recognizer is asked to decide which out of a population of N speakers is best classified as the unknown speaker. The decision may include a choice of "none of the above" (i.e., a choice that the specific speaker is not in a given closed set of speakers). The input speech material used for speaker recognition can be either text-dependent (text-constrained) or text-independent (text-free). In the text-dependent mode the speaker is asked to utter a prescribed text. The utterance is then The speaker-based VQ codebook generation can be summarized as follows: Given a set of I training feature vectors, {a1,a2 a) characterizing the variability of a speaker, we want to find a partitioning of the feature vector space, {S1,S2 SM}, for that particular speaker where, 5, the whole feature space is represented as S S1 US2 U . . . US. Each partition, S, forms a convex, nonoverlapping region and every vector inside S is represented by the respo5'tis'1g ceatcoid. vector, b1, cf 5,. The p titioning is done in such a way that the average distortion